As many companies adopted hybrid work arrangements during and after the COVID-19 outbreak, interest in Metaverse applications for virtual offices grew considerably. Along with this growing interest, the risk of data breaches has also increased, as virtual offices often handle confidential documents for businesses. For this reason, existing studies have explored Metaverse user authentication methods; however, their methods suffer from several limitations, such as the need for additional sensors and one-time authentication. Therefore, this paper proposes a novel behavioral authentication framework for secure Metaverse workspaces. The proposed framework adopts keyboard typing behavior that is common in the office and does not cause fatigue to users as an authentication factor to afford active and continuous user authentication. Based on our evaluation, the user identification accuracy achieved an average of approximately 95% among 11 of 15 participants, with the highest-performing user reaching an accuracy of 99.77%. In addition, the proposed framework achieved an average false acceptance rate of 0.41% and a false rejection rate of 4.02%. It was also evaluated with existing studies using requirements for user authentication in the Metaverse to demonstrate its strengths. Therefore, this framework can fully ensure a secure Metaverse office by preventing unauthenticated users.

Internet of Things (IoT) platforms are being actively designed to provide various services to users. However, most IoT platforms are developed in independent environments, causing difficulties in their interoperability. Even though heterogeneous IoT platforms employ the same attribute-based access control (ABAC) model to protect their resources, the attribute representation methods used by these platforms differ. Consequently, a semantic interoperability problem arises, wherein users of one platform cannot access the resources from other platforms, even though these users possess valid attributes. Therefore, this paper proposes a framework that can interoperate by understanding different attribute representation methods among heterogeneous IoT platforms that employ the same ABAC model. Our framework uses self-adaptive modules monitor-analyze-plan-execute over a shared knowledge (MAPE-K) to solve semantic interoperability problems arising from differences in attribute representations, even with valid attributes, enabling access to resources between heterogeneous IoT platforms.